A problem today for many practitioners, particularly in the science disciplines, is the scarcity of time to review the large volumes of information that are being collected. For example, modern methods in the life and chemical sciences are producing data at an unprecedented pace. This data may include not only text information, but also DNA sequences, protein sequences, numerical data (e.g., from gene chip assays), and categoric data.
Effective and timely use of this array of information is no longer possible using traditional approaches, such as lists, tables, or even simple graphs. Furthermore, it is clear that more valuable hypotheses can be derived by simultaneous consideration of multiple types of experimental data (e.g., protein sequence in addition to gene expression data), a process that is currently problematic with large amounts of data.
Visualization-based tools for analyzing data are discussed in, for example, Nielson G M, Hagen H, Muller H, eds., (1997) Scientific Visualization, IEEE Computer Society, Los Alamitos); (Becker R A, Cleveland W S (1987) Brushing Scatterplots, Technometrics 29:127–142; Cleveland W S (1993) Visualizing Data, Hobart Press, Summit, N J); (Bertin J (1983) Seminology of Graphics, University of Wisconsin Press, London; Cleveland W S (1993) Visualizing Data, Hobart Press, Summit, N J). These tools have focused largely on data characterization, and have provided limited user interactivity. For example, the user may gain access to underlying information by selecting an item with a pointer.
These tools, however, have significant drawbacks. Although current tools can handle certain data types (e.g., text, or numerical data), they do not allow a user to interact with disparate data types (i.e., text, numerical, categoric, and sequence data) within an integrated data analysis, mining, and visualization framework. Furthermore, these tools do not allow a user to interact well between different visualizations in the manner required to gain knowledge.
What is needed, therefore, is a tool that allows a user to analyze, mine, link, and visualize information of disparate data types within an integrated framework.